Detection of wind turbine towers in remote sensing based on YOLOv3 model under scale and density constraints
CHEN Jing1,2(), CHEN Jingbo1, MENG Yu1, DENG Yupeng1,2, JIE Yongshi1,2, ZHANG Yi1,2
1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China 2. School of Electronic,Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101400, China
The distribution of wind farms is an important basis for the monitoring and early warning of wind power investment, the analyses of land occupation, and the assessment of clean energy consumption capacity. Remote sensing technology serves as an effective method for extracting wind farm distribution on a large scale. As the remote sensing interpretation marks of wind farms, wind turbine towers are a kind of multi-scale targets in high-resolution images. However, their characteristics greatly differ due to the effects of image acquisition time, illumination conditions, and surface coverage. Therefore, it's difficult to automatically detect wind turbine towers in remote sensing images. Aiming at the above problems, this paper proposed an automatic detection method of wind turbine towers based on the YOLOv3 model, and the steps are as follows. Firstly, determine the sample construction conditions and the target scale of wind turbine towers according to the analyses of the remote sensing characteristics of a wind farm. Secondly, optimize the depth of the feature extraction network of the YOLOv3 model to improve the characterization capacity of multi-scale targets. Finally, suppress false detections using the DBSCAN density clustering algorithm according to the density difference between noise and wind turbine towers. The experimental results show that the proposed method exhibits superiority over the benchmark models such as Faster R-CNN and FPN. With a detection accuracy rate of 96%, a recall rate of 94%, and F1 of 95%, the proposed method has good effects for the detection of small targets in the remote sensing images with complex background.
陈静, 陈静波, 孟瑜, 邓毓弸, 节永师, 张懿. 尺度和密度约束下基于YOLOv3的风电塔架遥感检测方法[J]. 自然资源遥感, 2021, 33(3): 54-62.
CHEN Jing, CHEN Jingbo, MENG Yu, DENG Yupeng, JIE Yongshi, ZHANG Yi. Detection of wind turbine towers in remote sensing based on YOLOv3 model under scale and density constraints. Remote Sensing for Natural Resources, 2021, 33(3): 54-62.
Chen J B, Yue A Z, Wang C Y, et al. Wind turbine extraction from high spatial resolution remote sensing images based on saliency detection[J]. Journal of Applied Remote Sensing, 2018, 12(1):016041-016041.
Yu Y, Ai H, He X J, et al. Attention-based feature pyramid networks for ship detection of optical remote sensing image[J]. Journal of Remote Sensing, 24(2):107-115.
Dong B, Xiong F G, Han X, et al. Research on remote sensing building detection based on improved Yolo v3 algorithm[J]. Computer Engineering and Applications, 2020, 56(18):209-213.
Zhang P. Airport detection in optical remote sensing images with convolution neural network[D]. Beijing:National University of Defense Technology, 2016.
Han Z H. Research on small target detection algorithm based on statistical features and bridge method[D]. Changchun:Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences, 2019.
[8]
Barret Z, Ekin D C, Golnaz G, et al. Learning data augmentation strategies for object detection:America,US2019354817[P]. 2019-11-21.
Zhang Y H, Yang M. Information geometry method for ship detection in SAR images[J]. Journal of Image and Graphics, 2020, 25(1):206-213.doi: 10.11834/jig.190131.
doi: 10.11834/jig.190131
Xie Q F, Yao G Q, Zhang M. Research on high resolution image object detection technology based on Faster R-CNN[J]. Remote Sensing for Land and Resources, 2019, 31(2):38-43.doi: 10.6046/gtzyyg.2019.02.06.
doi: 10.6046/gtzyyg.2019.02.06
Zhang Z Q, Liu Y Y, Pan C C, et al. Application of improved YOLO V3 in aircraft recognition of remote sensing images[J]. Electronics Optics and Control, 2019, 26(4):28-32.doi: 10.3969/j.issn.1671-637X.2019.04.006.
doi: 10.3969/j.issn.1671-637X.2019.04.006
Chen H. A study on methods of SAR image target automatic detection and recognition[D]. Beijing:National Space Science Center,Chinese Academy of Sciences, 2019.
[14]
吴柳青. 基于多尺度多特征融合的遥感影像建筑物自动提取[D]. 武汉:武汉大学, 2018.
Wu L Q. Automatic building extraction of remote sensing images based on fused multi-scale and multi-feature[D]. Wuhan:Wuhan University, 2018.
Shen Y. Research on object detection algorithm of remote sensing images based on deep learning[D]. Shenzhen:Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, 2020.
[16]
Ren S Q, He K M, Girshick R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
doi: 10.1109/TPAMI.2016.2577031
[17]
Xie Y, Cai J, Bhojwani R, et al. A locally-constrained YOLO framework for detecting small and densely-distributed building footprints[J]. International Journal of Geographical Information Science, 2020, 34(4):777-801.
doi: 10.1080/13658816.2019.1624761
[18]
Wu Z, Chen X, Gao Y, et al. Rapid target detection in high resolution remote sensing images using YOLO model[J]. International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2018, 42:3.
Chen H Y, Liu Z Y, Guo W W, et al. Fast detection of ship targets for large-scale remote sensing image based on a cascade convolutional neural network[J]. Journal of Radars, 2019, 8(3):413-424.doi: 10.12000/JR19041.
doi: 10.12000/JR19041
2021, Vol. 33 
), 陈静波1, 孟瑜1, 邓毓弸1,2, 节永师1,2, 张懿1,2
